Predictive Battery Analytics Software

🚀 Energsoft specializes in creating custom software solutions for businesses of all sizes — from startups to global enterprises. Our expert team of developers can build products from scratch or seamlessly integrate with your existing systems to deliver exactly what your organization needs.

🎓 Born as a spin-out from the University of Washington in 2018, Energsoft combines deep academic research with real-world engineering. Since our founding, we’ve built a proprietary battery data repository with over 550,000 batteries — spanning diverse chemistries, sizes, and architectures collected from our customers and partners worldwide.

💡 This extensive dataset powers our AI-driven modeling and predictive analytics, helping battery and energy innovators accelerate R&D, reduce costs, and bring sustainable technologies to market faster.

🌎 Whether you’re working on next-generation EVs, energy storage systems, or consumer electronics, Energsoft empowers your team to unlock data-driven insights and optimize performance at every step.

📊 Import Your Experimental Data Seamlessly — Energsoft supports data ingestion from leading electrochemical testing systems such as Solatron, Gamry, and BioLogic.

🧠 Optimize with Intelligence — Use our built-in optimization library or create a simple loop to fine-tune parameters and models. Energsoft empowers researchers to easily calibrate and validate their results using robust, automated optimization tools.

⚡ EIS-Driven Insights for Better Batteries — Electrochemical Impedance Spectroscopy (EIS) remains a cornerstone of modern battery diagnostics and optimization. Through Energsoft’s advanced analytics platform, AI-powered impedance modeling transforms raw EIS data into actionable insights that drive improvements in reliability, efficiency, and performance prediction.

🏭 Scalable & Industrial-Grade — By integrating EIS modeling with machine learning, Energsoft revolutionizes how manufacturers and researchers analyze batteries—making testing faster, more precise, and scalable across large datasets and production environments.

Provide the flexibility to plug in new model components as needed, allowing you to extend the code for different types of experiments. Measure internal resistance, study charge/discharge cycles, and identify performance-limiting processes. It can help optimize battery designs, understand aging mechanisms, and test materials under various operating conditions

We provide cloud solutions that help businesses streamline their operations and increase productivity. Our team can help you migrate your data to the cloud, optimize your cloud infrastructure, and ensure that your cloud-based applications are running smoothly.

The input data could simply look like this: "Frequency (Hz), Real(Z) (Ohms), Imag(Z) (Ohms)"

Energsoft's platform offers several advantages when handling EIS data, including:

• Scalability: Processes vast amounts of impedance data efficiently.
• Compatibility: Integrates with major EIS testing instruments.
• User-Friendly Interface: Enables easy visualization and interpretation of complex datasets.
• Advanced AI-Powered Predictions: Improves battery reliability by forecasting degradation patterns.

EIS is a powerful technique used in battery research and diagnostics to analyze electrochemical systems. By applying a small alternating current (AC) signal over a range of frequencies and measuring the system's response, EIS helps characterize battery behavior, detect faults, and predict battery lifespan. This non-destructive method provides insights into charge transfer resistance, electrolyte conductivity, and other key parameters essential for battery performance assessment.

We provide 24/7 support to our clients to ensure that their systems are always up and running. Our team of experts is available around the clock to help you with any technical issues that may arise. For instance, if you are monitoring batteries in the field and need remote access to the battery EIS data.

Our team of expert developers can create bespoke software solutions tailored to the unique needs of your business. From concept to deployment, we work with you every step of the way to ensure your software meets your battery exact requirements.

Here is a small example of the following Rendles and Nelder-Mead  techniques:

1. The program models an electrochemical system (battery) using a Randles Circuit.
2. It calculates impedance based on resistors and capacitance.
3. It compares computed impedance to real-world measurements and adjusts the parameters.
4. It finds the best values of R1, R2, and C using optimization.

This is useful for battery diagnostics, quality testing, and predicting battery lifespan.

Fitted Parameters: R1 = 9.8, R2 = 105.3, C = 9.5e-07
This helps researchers and engineers better understand battery behavior and optimize performance! 🚀

def randles_circuit(frequencies, R_s, R_ct, C_dl):

    """Simulates impedance response of a Randles circuit model."""

    omega = 2 * np.pi * frequencies

    Z_cdl = 1 / (1j * omega * C_dl)  # Capacitance impedance

    Z = R_s + (R_ct * Z_cdl) / (R_ct + Z_cdl)  # Randles model

    return Z

def fit_eis_data(frequencies, impedance):

    """Fits the Randles circuit model to given EIS data."""

    def objective(params):

        R_s, R_ct, C_dl = params

        modeled_impedance = randles_circuit(frequencies, R_s, R_ct, C_dl)

        return np.concatenate([(modeled_impedance.real - impedance.real), 

                               (modeled_impedance.imag - impedance.imag)])

    initial_guess = [5, 50, 1e-6]

    fitted_params, _ = opt.leastsq(objective, initial_guess)

    return fitted_params

public static void FitModel(double[] frequencies, Complex[] measuredImpedance)

{

        // Initial guesses for parameters [R1, R2, C]

        double[] initialGuess = { 10.0, 100.0, 1e-6 };

        var objective = ObjectiveFunction.Value(x => CostFunction(x, frequencies, measuredImpedance));

        var optimizer = new NelderMeadSimplex(1e-6, 1000);

        var result = optimizer.FindMinimum(objective, initialGuess);

        // Fitted parameters

        double R1_fitted = result.MinimizingPoint[0];

        double R2_fitted = result.MinimizingPoint[1];

        double C_fitted = result.MinimizingPoint[2];

        // Display results

        Console.WriteLine($"Fitted Parameters: R1 = {R1_fitted}, R2 = {R2_fitted}, C = {C_fitted}");

}